Adjustment of Nigeria population censuses using mathematical methods
Bibliographic record
Abstract
This paper is on the adjustment of reported populations in Nigerian censuses. The ultimate objective is to provide reliable base populations which may be used to provide improved estimates of demographic parameters. Mathematical methods are applied to obtain adjusted from the reported populations by sex and age in single years and 5-year age groups in the 1963, 1991 and 2006 Nigerian censuses. Thereafter, the adjusted data were subjected to re-evaluation. The results of the re-evaluation of the adjusted data show that the qualities of the adjusted data as well as the estimates of demographic parameters have improved. For data by age in single years, the preference for the end digits 0 and 5 appear to have been reduced while the accuracy index shows that quality of adjusted data by 5- year age groups has improved across the censuses. It has therefore, been recommended that the adjusted data be used for estimation of demographic parameters and population projection among others.Ce document est sur l'ajustement de la population rapportée dans des recensements nigérians. L'objectif final est de fournir les bases populations dignes de confiance qui peuvent être employées pour donner des évaluations améliorées des paramètres démographiques. Des méthodes mathématiques ont été appliquées pour obtenir ajusté des populations rapportées par sexe et par âge dans les seules anneés et de la tranche de cinq ans dans les recensements nigérians de 1963, 1991 et 2006. Ensuite, les données ajustées ont été soumises à la réévaluation et utilisées pour obtenir des évaluations des paramètres démographiques. Les résultats de la réévaluation des données ajustées prouvent que les qualités des données aussi bien que des évaluations ajustées de quelques paramètres démographiques se sont améliorées. Il est recommandé alors, que les données ajustées soient employées pour l'évaluation des paramètres et de la projection de population démographiques parmi d’autres.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".